CICERO contains 53,000 inferences for five commonsense dimensions -- cause, subsequent event, prerequisite, motivation, and emotional reaction -- collected from 5600 dialogues. It involves two challenging generative and multi-choice alternative selection tasks for the state-of-the-art NLP models to solve. Download the dataset using this link.
Dataset Description The dataset described in the provided text is focused on social media polls collected from Weibo, a popular Chinese microblogging platform. The dataset aims to empirically study social media polls and analyze user engagement patterns. ### Characteristics of the Dataset - Size: The dataset consists of 20,252 polls collected from 1,860 users on Weibo. - Data Collection: The polls were obtained by sampling Weibo posts containing polls and examining the posting history of the authors. The dataset also includes comments on each post. - Sparsity: The dataset faces the challenge of sparse distribution of polls on Weibo, as less than 0.1% of the randomly gathered posts contained polls. - Content: The dataset includes user-generated polls with questions, answer choices, and corresponding votes. The polls often incorporate trendy hashtags to attract user attention and cover various topics, including social events, public emergencies (such as the COVID-19 outbreak), entertainment topics (celebrities, TV shows), and more. ### Motivations and Summary The motivation behind collecting this dataset is to explore social media polls on Weibo and analyze user engagement patterns. The study aims to understand how users interact with polls, the influence of polls on user engagement, and the types of topics that are more likely to contain polls. The dataset provides insights into user behavior on Weibo by examining factors such as the length of posts, comments, questions, and answers. It also highlights the preference for voting over commenting as a means of expressing opinions. The dataset's analysis suggests that posts with polls tend to attract more comments, likes, and reposts compared to posts without polls. ### Potential Use Cases This dataset can be useful for various research and practical applications, such as: - Social Media Analysis: Researchers can analyze the characteristics and dynamics of social media polls, understanding how they influence user engagement and the types of topics that attract poll creation. - User Engagement Studies: The dataset allows for the exploration of user behavior and preferences when it comes to interacting with polls, providing insights into the factors that drive user engagement on social media platforms. - Trend Analysis: By examining the hashtags associated with polls, the dataset can contribute to understanding social events, public emergencies, and entertainment trends on Weibo. - Marketing and Advertising: The dataset can assist marketers and advertisers in understanding user preferences and interests, enabling them to create targeted campaigns based on the popular topics identified in the dataset. Please note that the actual contents and specific applications of the dataset may vary based on further analysis and research conducted by users.
To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games